code: def loss(self, pred, y):
      loss = 0
      for key in pred:
          p = torch.sigmoid(pred[key])
          loss += self.loss_fn(p, y[key].type(pred[key].dtype))
      return loss
  
imports: import argparse
  import copy
  import time
  import numpy
  import networkx
  import sklearn.metrics
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import Planetoid
  from torch_geometric.datasets import TUDataset
  import torch_geometric.transforms
  import torch_geometric.nn
  from deepsnap.hetero_graph import HeteroGraph
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  from deepsnap.hetero_gnn import HeteroSAGEConv, HeteroConv, forward_op
label_desc: Use image node_id 3 for calling the HeteroGNN obj's underlying member method code with example usage: obj.loss(pred, y) and returns: loss